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Evaluation of Large Language Models: STEM education and Gender Stereotypes

arXiv 2406.10133 ↗First posted 14 Jun 2024cs.CL cs.AIcited by 0

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Abstract (arXiv)

Large Language Models (LLMs) have an increasing impact on our lives with use cases such as chatbots, study support, coding support, ideation, writing assistance, and more. Previous studies have revealed linguistic biases in pronouns used to describe professions or adjectives used to describe men vs women. These issues have to some degree been addressed in updated LLM versions, at least to pass existing tests. However, biases may still be present in the models, and repeated use of gender stereotypical language may reinforce the underlying assumptions and are therefore important to examine further. This paper investigates gender biases in LLMs in relation to educational choices through an open-ended, true to user-case experimental design and a quantitative analysis. We investigate the biases in the context of four different cultures, languages, and educational systems (English/US/UK, Danish/DK, Catalan/ES, and Hindi/IN) for ages ranging from 10 to 16 years, corresponding to important educational transition points in the different countries. We find that there are significant and large differences in the ratio of STEM to non-STEM suggested education paths provided by chatGPT when using typical girl vs boy names to prompt lists of suggested things to become. There are generally fewer STEM suggestions in the Danish, Spanish, and Indian context compared to the English. We also find subtle differences in the suggested professions, which we categorise and report.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 15 of its 42 references that are in the map. First posted on arXiv 14 Jun 2024.

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References · 15

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
  2. 2021Ethical and social risks of harm from Language Modelscited by 966
  3. 2020StereoSet: Measuring stereotypical bias in pretrained language modelscited by 924
  4. 2021Persistent Anti-Muslim Bias in Large Language Modelscited by 377
  5. 2019Lipstick on a Pig: Debiasing Methods Cover up Systematic Gender Biases in Word Embeddings But do not Remove Themcited by 268
  6. 2023Gender bias and stereotypes in Large Language Modelscited by 187
  7. 2021RedditBias: A Real-World Resource for Bias Evaluation and Debiasing of Conversational Language Modelscited by 149
  8. 2023Should ChatGPT be Biased? Challenges and Risks of Bias in Large Language Modelscited by 148
  9. 2020Why Fairness Cannot Be Automated: Bridging the Gap Between EU Non-Discrimination Law and AIcited by 81
  10. 2023WinoQueer: A Community-in-the-Loop Benchmark for Anti-LGBTQ+ Bias in Large Language Modelscited by 42
  11. 2022Validating Large Language Models with ReLMcited by 33
  12. 2023Unveiling Gender Bias in Terms of Profession Across LLMs: Analyzing and Addressing Sociological Implicationscited by 29
3 more
  1. 2023She Elicits Requirements and He Tests: Software Engineering Gender Bias in Large Language Modelscited by 12
  2. 2022Selection Bias Induced Spurious Correlations in Large Language Modelscited by 9
  3. 2023BAD: BiAs Detection for Large Language Models in the context of candidate screeningcited by 8

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